How does sampling bias affect data mining outcomes?

Sampling bias leads to models that do not generalize to the true population.

Sampling bias occurs when training data is not representative of the real-world distribution. This distorts learned patterns and leads to misleading conclusions. In data mining, biased samples can arise from selection bias, survivorship bias, or measurement bias, significantly affecting clustering, classification, and association rule mining outcomes.